1. Field of the Invention
We disclose a system whereby software piracy can be deterred while copying and purchasing can be encouraged. User to user copying plays an important positive role in the present system, and in the shareware industry, and as “piracy” it plays an important negative role in other shareware- and internet-based marketing schemes. The present invention discloses methods whereby software copying and purchasing can be tracked, studied, and rewarded, and methods whereby software lineages can become adapted to their environments.
2. Introduction
In an increasingly wired world, software can be reproduced and distributed worldwide, in minutes, and at little cost. For intellectual property vendors, these economies are problematic: freely copyable software typically gives customers little incentive for payment, and it positions piracy as the path of least resistance.
We disclose here a method for piracy prevention and purchase encouragement without loss of copyability. The system gives vendors round-the-clock and round-the-globe vending and fulfillment services, as well as secondary and tertiary sales from copied software. And the system gives users convenient access to digital products which might otherwise be less readily available, lets them try before buying, provides incentives for purchasing, and rewards them with increased benefits minutes after they decide to buy.
As disclosed, the basic idea is to “lock” selected features of a digital product such that the password required to access those features is unique to a particular product and context, and such that the password can be instantly and conveniently purchased and acquired by telephone, email, modem, etc. Users can thus evaluate locked products in their still-locked “demo mode” and unlock them in minutes. Vendors can encourage customers to pass copies on to other potential customers, because when the context changes, SoftLocked products automatically revert to demo mode.
Using this system, intellectual property owners can allow their products to be freely redistributed without losing control over their conditions of use, and without foregoing the ability to demand and receive fair compensation.
The Importance of Reproduction.
The dynamics, “flow,” and reproduction of software through the information marketplace is not well understood, in part because it is difficult to study. Yet it is of significant economic and scientific importance. User-to-user copying, software reproduction, “Pass-along”, etc., plays an important positive role in our system, and in other shareware- and internet-based marketing schemes. Unauthorized software reproduction (“piracy”) costs software producers billions of dollars annually, and discourages the release of other digital properties. More generally, social scientists have long recognized that the spread and evolution of reproducible patterns of information (variously known as “memes”, “culturgens”, etc.) is the very essence of culture and cultural evolution. The internet is a recent and arguably revolutionary new arena in which such processes occur with unprecedented speed; methods for investigating and exploiting this new information ecology are therefore sorely needed.
Software marketing, and the study of the information economy would also be greatly enhanced by a system which tracked the flow of copies from person to person and from computer to computer. The ability to track “chains of copying” would aid the investigation of suspected piracy, the study of data flow through unregulated and/or unmonitored digital systems, the auditing of service providers, the exploration of marketing and advertising strategies, and the implementation of multi-level marketing schemes which pay commissions to individuals whose copying and distribution efforts result in increased sales, etc.
It is not difficult to imagine that a piece of software could track its own “travels” from person to person and from computer to computer, for as used here “software” refers either to executable programs into which self-tracking algorithms and technologies might be embedded, or data documents designed for processing by an executable programs, into which self-tracking algorithms and technologies might be embedded.
The present invention discloses a number of suitable self-tracking algorithms and technologies by which one might determine of a product's “chain of copying,” “lineage”, or “pedigree” within and between processing devices and information networks.
As implied by the terms “lineage” and “pedigree”, the reproduction and distribution of software in information networks is similar to the reproduction and spread of organisms in nature. “Computer viruses” are so-called precisely because of this similarity. The present invention exploits and extends this similarity. As will be disclosed below, it solves the problem of tracking, allows digital products to more fully exploit the “drawinian” potential of the information marketplace, and has other applications. To lay the groundwork, some basic concepts of biological systems will now be reviewed.
At a certain level of abstraction, biological reproduction is digital copying: the genetic code is in fact a digital system, and gene pools, species, and ecosystems are in fact highly-evolved networks which support the copying and spread of these codes. In biological parlance, the “genome” is the encoding of a single organism's complete genetic makeup Although genome sizes vary widely from one species to the next, genome size is virtually constant within a given species. In sexually reproducing species, each parent contributes a random half-genome which is recombined to produce the offspring's complete genome. In asexually reproducing organisms, there is no systematic randomization, and a single parent's entire genome is simply replicated in the offspring, albeit with copying errors known as mutations. In both sexual and asexual species, the size of the offspring's genome is therefore the same size as the parents' genome. In both cases too, the content of the offspring's genome (that is, the particular genetic patterns which characterize the individual) is the same as the parents' content except for the randomizing influences of recombination and and/or mutation.
The copying of artificial digital products is most like asexual reproduction insofar as each instance of a program or document (henceforth, “software-instance”) can be copied, and each copy can itself become the basis for a “chain of copying” analogous to a biological lineage. Random variation through copying error is usually guarded against in the world of software, but it can occur, and it can certainly be designed into artificial system, as disclosed here.
In biology, random genetic variation plays a crucial role in evolution by natural selection, because it produces functional variations in organisms which are transmitted to offspring. These functional variations have influences on success and biological reproduction, which therefore produces differential reproduction of selected genetic variations, which produces adaptive evolution of species, lineages, etc. Similar processes of variation and selection occur in other systems, and are an increasingly important area of research in theoretical and applied computer science. The relevant disciplines are known as “Artificial Life”, “Evolutionary Programming”, “Genetic Algorithms”, “General Evolution Theory”, etc.
Through the study of the genetic sequences in individual genome, biologists have determined that random variation occurs each time reproduction occurs, and that these variations propagate and accumulate though successive generations. By comparing the sequences in one individual with sequences found in other individuals, it is possible to deduce and reconstruct the historical sequence of copying errors which derived those sequences from a common ancestor. The procedures involved are amply documented and widely employed in the scientific literature, so we will only summarize some basic heuristics here.
The degree of similarity between two individuals can be used as an index of the number of copying events which intervene between them. For example, since only one copying event intervenes between parent and offspring, there will be relatively little variation between them, whereas the genomes of more distant relatives tend to be less similar, because many copying events intervene between them. With further assumptions about mutation rate one can estimate the precise number of copying events intervening between two individuals based upon use the degree of genetic dissimilarity between them.
Moreover, because genes can be distinguished by their position within a genome, analysis of the specific patterns of information shared by two individuals provides further clues to the ancestry, or copying history, of those patterns. When an unusual (or less than universal) genetic sequence shows up in two individuals in the same genomic location, it is probably that those individuals share a common ancestor, and that that ancestor bore the same trait. In this way, the genome of the common ancestor can be determined probabilistically.
Finally, by correlating this information with knowledge of confirmed individuals, and through other means, it is possible to reconstruct with a high degree of probability the historical sequence of copying errors which intervened between individuals with similar, but non-identical genomes. The genetic history of a lineage can thus be reconstructed.
Those familiar with biology and biotechnology will know that through methods of the sort sketched above, and through other techniques with similar bases in biology and mathematics, it is possible to reconstruct biological pedigrees with a high degree of accuracy based on very limited samples of populations. The techniques work even though genome size remains constant, and even though the genomes do not contain a systematic record of their own pedigrees.
Although these biological techniques have been developed for the analysis of relatively “messy” biological systems, they can be applied to any system in which idiosyncratic patterns of information accumulate within a reproducing lineage.
We disclose several methods of achieving these ends, and disclose further methods which eliminate the need for retrieval of complete software-instances from the field. Another elaboration will couple the random error mechanism with a mechanism of selection in order to increase the fitness of products to their environment, the usefulness of products to their customers, and the profitability of these products to their creators. Finally, a last elaboration will allow vendors to use the information gathered in order to modify the characteristics of already-released software.